SOTAVerified

Intent Detection

Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.

Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

Papers

Showing 251300 of 330 papers

TitleStatusHype
Joint Multiple Intent Detection and Slot Filling via Self-distillation0
Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks0
Key-phrase boosted unsupervised summary generation for FinTech organization0
LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling0
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings0
Learning Multimodal Confidence for Intention Recognition in Human-Robot Interaction0
Few-shot Pseudo-Labeling for Intent DetectionCode0
A Unified Framework for Multi-intent Spoken Language Understanding with promptingCode0
CM-Net: A Novel Collaborative Memory Network for Spoken Language UnderstandingCode0
PSCon: Product Search Through ConversationsCode0
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
Learning to Select from Multiple OptionsCode0
Towards Multi-label Unknown Intent DetectionCode0
From Disfluency Detection to Intent Detection and Slot FillingCode0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction NetworkCode0
SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLUCode0
Zero-shot User Intent Detection via Capsule Neural NetworksCode0
Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue systemCode0
Learning Discriminative Representations and Decision Boundaries for Open Intent DetectionCode0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot InteractionCode0
A Closer Look at Few-Shot Out-of-Distribution Intent DetectionCode0
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrenceCode0
User-in-the-loop Adaptive Intent Detection for Instructable Digital AssistantCode0
MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLUCode0
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understandingCode0
Churn Intent Detection in Multilingual Chatbot Conversations and Social MediaCode0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language UnderstandingCode0
Representation based meta-learning for few-shot spoken intent recognitionCode0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot FillingCode0
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-trainingCode0
Effectiveness of Pre-training for Few-shot Intent ClassificationCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot FillingCode0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain DetectionCode0
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue SystemsCode0
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correctionCode0
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot FillingCode0
All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding ParadigmCode0
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent RecognitionCode0
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent DetectionCode0
RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-TrainingCode0
Integrating Text and Image: Determining Multimodal Document Intent in Instagram PostsCode0
Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting FrameworkCode0
Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ RetrievalCode0
Tri-level Joint Natural Language Understanding for Multi-turn Conversational DatasetsCode0
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot FillingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Bi-model with decoderAccuracy98.99Unverified
2Transformer-CapsuleAccuracy98.89Unverified
3Attention Encoder-Decoder NNAccuracy98.43Unverified
4Joint model with recurrent slot label contextAccuracy98.4Unverified
5CTRANAccuracy98.07Unverified
6Joint BERT + CRFAccuracy97.9Unverified
7SF-IDAccuracy97.76Unverified
8SF-ID (BLSTM) networkAccuracy97.76Unverified
9JointBERT-CAEAccuracy97.5Unverified
10Joint BERTAccuracy97.5Unverified
#ModelMetricClaimedVerifiedStatus
1SSRANAccuracy98.4Unverified
2BiSLUAccuracy97.8Unverified
3DGIFAccuracy97.8Unverified
4Co-guiding NetAccuracy97.7Unverified
5TFMNAccuracy97.7Unverified
6TFMN (PACL)Accuracy97.4Unverified
7MISCAAccuracy97.3Unverified
8Uni-MISAccuracy97.2Unverified
9SLIMAccuracy97.2Unverified
10UGENAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1DGIFAccuracy83.3Unverified
2UGENAccuracy83Unverified
3TFMN (PACL)Accuracy82.9Unverified
4SLIM (PACL)Accuracy81.9Unverified
5BiSLUAccuracy81.5Unverified
6TFMNAccuracy79.8Unverified
7RoBERTa (PACL)Accuracy79.1Unverified
8Co-guiding NetAccuracy79.1Unverified
9Uni-MISAccuracy78.5Unverified
10SLIMAccuracy78.3Unverified
#ModelMetricClaimedVerifiedStatus
1CTRANAccuracy99.42Unverified
2Stack-Propagation (+BERT)Accuracy99Unverified
3JointBERT-CAEAccuracy98.3Unverified
4AGIFAccuracy98.1Unverified
5LIDSNetAccuracy98Unverified
6Stack-PropagationAccuracy98Unverified
7SF-IDAccuracy97.43Unverified
8SF-ID (BLSTM) networkAccuracy97.43Unverified
9Capsule-NLUAccuracy97.3Unverified
10Slot-Gated BLSTM with AttensionAccuracy97Unverified
#ModelMetricClaimedVerifiedStatus
1plain-LSTMF10.89Unverified
2linear-NgramsF10.87Unverified
3glove-LSTMF10.86Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.42Unverified
2OCaTS (kNN-GPT-4)Accuracy (%)82.7Unverified
#ModelMetricClaimedVerifiedStatus
1JointBERT-CAEIntent Accuracy97.7Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)89.79Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)84.01Unverified
#ModelMetricClaimedVerifiedStatus
1CM-NetAcc94.56Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)97.12Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.84Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.62Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuracy94.27Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.57Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)87.41Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)82.45Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuarcy85.02Unverified
#ModelMetricClaimedVerifiedStatus
1General SLU Model w/ ProfileAccuracy0.85Unverified